To model decision problems involving uncertainty and probability, we propose stochasticconstraintprogramming. Stochasticconstraintprograms contain both decision variables (which we can set) and stochastic variables (which follow some probability distribution), and combine together the best

"... To model combinatorial decision problems involving uncertainty and probability, we extend the stochastic constraint programming framework proposed in [Walsh, 2002] along a number of important dimensions (e.g. to multiple chance constraints and to a range of new objectives). We also provide a n ..."

To model combinatorial decision problems involving uncertainty and probability, we extend the stochasticconstraintprogramming framework proposed in [Walsh, 2002] along a number of important dimensions (e.g. to multiple chance constraints and to a range of new objectives). We also provide a

by
Suresh Man, Armagan Tarim, Toby Walsh
- In Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence, 2003

"... To model combinatorial decision problems involving uncertainty and probability, we extend the stochastic constraint programming framework proposed in [Walsh, 2002] along a number of important dimensions (e.g. to multiple chance constraints and to a range of new objectives). We also provide a new (bu ..."

To model combinatorial decision problems involving uncertainty and probability, we extend the stochasticconstraintprogramming framework proposed in [Walsh, 2002] along a number of important dimensions (e.g. to multiple chance constraints and to a range of new objectives). We also provide a new

"... To model combinatorial decision problems involving uncertainty and probability, we extend the stochastic constraint programming framework proposed in [Walsh, 2002] along a number of important dimensions (e.g. to multiple chance constraints and to a range of new objectives). We also provide a n ..."

To model combinatorial decision problems involving uncertainty and probability, we extend the stochasticconstraintprogramming framework proposed in [Walsh, 2002] along a number of important dimensions (e.g. to multiple chance constraints and to a range of new objectives). We also provide a

To model combinatorial decision problems involving uncertainty and probability, we introduce scenario based stochasticconstraintprogramming. Stochasticconstraintprograms contain both decision variables, which we can set, and stochastic variables, which follow a discrete probability distribution

by
Roberto Rossi, S. Armagan Tarim, Brahim Hnich, Steven Prestwich
- In Proceedings of the 14th International Conference on the Principles and Practice of Constraint Programming

"... Abstract. Cost-based filtering is a novel approach that combines techniques from Operations Research and Constraint Programming to filter from decision variable domains values that do not lead to better solutions [7]. Stochastic Constraint Programming is a framework for modeling combinatorial optimi ..."

Abstract. Cost-based filtering is a novel approach that combines techniques from Operations Research and ConstraintProgramming to filter from decision variable domains values that do not lead to better solutions [7]. StochasticConstraintProgramming is a framework for modeling combinatorial

"... Abstract. We adopt Benders ’ decomposition algorithm to solve scenariobased Stochastic Constraint Programs (SCPs) with linear recourse. Rather than attempting to solve SCPs via a monolithic model, we show that one can iteratively solve a collection of smaller sub-problems and arrive at a solution to ..."

Abstract. We adopt Benders ’ decomposition algorithm to solve scenariobased StochasticConstraintPrograms (SCPs) with linear recourse. Rather than attempting to solve SCPs via a monolithic model, we show that one can iteratively solve a collection of smaller sub-problems and arrive at a solution

"... Stochastic Logic Programs (SLPs) have been shown to be a generalisation of Hidden Markov Models (HMMs), stochastic context-free grammars, and directed Bayes' nets. A stochastic logic program consists of a set of labelled clauses p:C where p is in the interval [0,1] and C is a first-order r ..."

Stochastic Logic Programs (SLPs) have been shown to be a generalisation of Hidden Markov Models (HMMs), stochastic context-free grammars, and directed Bayes' nets. A stochastic logic program consists of a set of labelled clauses p:C where p is in the interval [0,1] and C is a first